Abstract
In order to avoid the fatigue operation of microgrid and ensure the application reliability of equipment components, a reliability detection model of micro grid soc droop control based on convolutional neural network is proposed. The convolutional neural network architecture is constructed. By defining small target parameters, the real-time tracking of target samples is realized, and the micro grid charging state operation target is identified. Improve the polarity detection conditions of capacitor equipment, according to the data acquisition and calibration processing results, match the micro grid operation data with the detection template, and achieve the design of micro grid soc droop control reliability detection model based on convolutional neural network. Comparative experimental results: under the effect of the convolutional neural network detection model, when the fatigue curve value reaches 0.18, the indicator device will flash abnormally, which can avoid the fatigue operation state of the microgrid, and has a prominent role in ensuring the reliability of the application of the micro grid soc droop control.
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Aknowledgement
1. Research on Energy Storage Control of DC Microgrid under the Background of “Dual Carbon”
2. Source: Undergraduate Innovation and Entrepreneurship Training Program of Dalian University of Science and Technology.
3. Power Distribution Strategy and Simulation Analysis of Multi-group Hybrid Energy Storage System in DC Microgrid.
4. Source: Basic Scientific Research Project of the Education Department of Liaoning Province in 2021 (Supported Project), Item No.: KYZ2141.
5. Application research of fractional-order gradient descent method in neural network control.
6. Source: The Education Department of Liaoning Province, Item No.: L2020010.
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Yan, Z., Song, C., Xu, Z., Wang, Y. (2024). Reliability Testing Model of Micro Grid Soc Droop Control Based on Convolutional Neural Network. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-031-50574-4_7
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DOI: https://doi.org/10.1007/978-3-031-50574-4_7
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